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74
A Simple Optimal Power Flow Model with Energy Storage
"... Abstract — The integration of renewable energy, such as wind power, into the electric grid is difficult because of the source intermittency and the large distance between generation sites and users. This difficulty can be overcome through a transmission network with largescale storage that not only ..."
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Cited by 23 (1 self)
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Abstract — The integration of renewable energy, such as wind power, into the electric grid is difficult because of the source intermittency and the large distance between generation sites and users. This difficulty can be overcome through a transmission network with largescale storage that not only transports power, but also mitigates against fluctuations in generation and supply. We formulate an optimal power flow problem with storage as a finitehorizon optimal control problem. We prove, for the special case with a single generator and a single load, that the optimal generation schedule will cross the timevarying demand profile at most once, from above. This means that the optimal policy will generate more than demand initially in order to charge up the battery, and then generate less than the demand and use the battery to supplement generation in final stages. This is a consequence of the fact that the marginal storage costtogo decreases in time. I.
Message passing for dynamic network energy management
, 2012
"... We consider a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its own dynamic constraints and objective, connected by lossy capacitated lines. The problem is to minimize the total network objective subject to the device and line constraints, over ..."
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Cited by 15 (0 self)
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We consider a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its own dynamic constraints and objective, connected by lossy capacitated lines. The problem is to minimize the total network objective subject to the device and line constraints, over a given time horizon. This is a large optimization problem, with variables for consumption or generation in each time period for each device. In this paper we develop a decentralized method for solving this problem. The method is iterative: At each step, each device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing its own objective function, augmented by a term determined by the messages it has received. We show that this message passing method converges to a solution when the device objective and constraints are convex. The method is completely decentralized, and needs no global coordination other than synchronizing iterations; the problems to be solved by each device can typically be solved extremely efficiently and in parallel. The method is fast enough that even a serial implementation can solve substantial problems
Economic dispatch solution using a genetic algorithm based on arithmetic crossover
 IEEE Porto PowerTech'2001, Paper No
, 2001
"... Abstract—In this paper, a new genetic approach based on arithmetic crossover for solving the economic dispatch problem is proposed. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves ..."
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Cited by 13 (2 self)
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Abstract—In this paper, a new genetic approach based on arithmetic crossover for solving the economic dispatch problem is proposed. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves the quality of the solution. The new genetic approach is compared with an improved Hopfield NN approach (IHN) [1], a fuzzy logic controlled genetic algorithm (FLCGA) [2], an advance engineeredconditioning genetic approach (AECGA) [3] and an advance Hopfield NN approach (AHNN) [4]. Index TermsEconomic dispatch, genetic algorithm, arithmetic crossover. I.
Environmentally Constrained Economic Dispatch via a Genetic Algorithm with Arithmetic Crossover
 IEEE Africon
, 2002
"... Abstract: Operating at absolute minimum cost can no longer be the only criterion for dispatching electric power due to increasing concern the environmental consideration. The environmentally constrained economic dispatch problem which accounts for minimization of both cost and emission is a multiple ..."
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Abstract: Operating at absolute minimum cost can no longer be the only criterion for dispatching electric power due to increasing concern the environmental consideration. The environmentally constrained economic dispatch problem which accounts for minimization of both cost and emission is a multiple objective function problem. In this paper, an improved Hopfield neural network which was described in [1] is applied to environmentally constrained economic dispatch problem. Sample test results are presented. 1.
Combined Economic And Emission Dispatch Using Evolutionary AlgorithmsA
 Case Study”, ARPN Journal of Engineering and Applied Sciences
"... An efficient and optimum economic operation of electric power generation systems has always occupied an important position in the electric power industry. This involves allocation of the total load between the available generating units in such a way that the total cost of operation is kept at a min ..."
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Cited by 6 (0 self)
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An efficient and optimum economic operation of electric power generation systems has always occupied an important position in the electric power industry. This involves allocation of the total load between the available generating units in such a way that the total cost of operation is kept at a minimum. In recent years this problem has taken a suitable twist as the public has become increasingly concerned with environmental matters, so that economic dispatch now includes the dispatch of systems to minimize pollutants, as well as to achieve minimum cost. This paper proposes a lambda based approach for solving the Combined Economic and Emission Dispatch (CEED) problem using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methodologies considering the power limits of the generator. The purpose of Combined Economic and Emission Dispatch (CEED) is to minimize both the operating fuel cost and emission level simultaneously while satisfying load demand and operational constraints. This multiobjective CEED problem is converted into a single objective function using a modified price penalty factor approach.
Ant Colony Optimization Applied on Combinatorial Problem for Optimal Power Flow Solution
, 2009
"... This paper presents an efficient and reliable evolutionarybased approach to solve the optimal power flow (OPF) combinatorial problem. The proposed approach employs Ant Colony Optimization (ACO) algorithm for optimal settings of OPF combinatorial problem control variables. Incorporation of ACO as a ..."
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Cited by 5 (2 self)
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This paper presents an efficient and reliable evolutionarybased approach to solve the optimal power flow (OPF) combinatorial problem. The proposed approach employs Ant Colony Optimization (ACO) algorithm for optimal settings of OPF combinatorial problem control variables. Incorporation of ACO as a derivativefree optimization technique in solving OPF problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed approach has been examined and tested on the standard IEEE 57bus test System with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.
Distributed generator coordination for initialization and anytime optimization in economic dispatch
, 2013
"... This paper considers the economic dispatch problem for a group of generator units communicating over an arbitrary weightbalanced digraph. The objective of the individual units is to collectively generate power to satisfy a certain load while minimizing the total generation cost, which corresponds ..."
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Cited by 4 (1 self)
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This paper considers the economic dispatch problem for a group of generator units communicating over an arbitrary weightbalanced digraph. The objective of the individual units is to collectively generate power to satisfy a certain load while minimizing the total generation cost, which corresponds to the sum of individual arbitrary convex functions. We propose a class of distributed Laplaciangradient dynamics that are guaranteed to asymptotically find the solution to the economic dispatch problem with and without generator constraints. The proposed coordination algorithms are anytime, meaning that its trajectories are feasible solutions at any time before convergence, and they become better and better solutions as time elapses. Additionally, we design the provably correct, distributed DETERMINE FEASIBLE ALLOCATION strategy to handle generator initialization and scenarios with addition and deletion of units. Several simulations illustrate our results.
Economic Load Dispatch using Bacterial Foraging Technique with Particle Swarm Optimization Biased Evolution
"... Abstract—This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space an ..."
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Cited by 3 (0 self)
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Abstract—This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by celltocell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and eliminationdispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods. Index Terms—Bacterial foraging technique, particle swarm optimization, economic load dispatch. I.
Solving the Economic Dispatch Problem by Using Differential Evolution
"... Abstract—This paper proposes an application of the differential evolution (DE) algorithm for solving the economic dispatch problem (ED). Furthermore, the regenerating population procedure added to the conventional DE in order to improve escaping the local minimum solution. To test performance of DE ..."
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Abstract—This paper proposes an application of the differential evolution (DE) algorithm for solving the economic dispatch problem (ED). Furthermore, the regenerating population procedure added to the conventional DE in order to improve escaping the local minimum solution. To test performance of DE algorithm, three thermal generating units with valvepoint loading effects is used for testing. Moreover, investigating the DE parameters is presented. The simulation results show that the DE algorithm, which had been adjusted parameters, is better convergent time than other optimization methods. Keywords—Differential evolution, Economic dispatch problem, Valvepoint loading effect, Optimization method. I.
A NEW GENETIC ALGORITHM WITH ARITHMETIC CROSSOVER TO ECONOMIC AND ENVIRONMENTAL ECONOMIC DISPATCH
"... This paper presents a new genetic approach based on arithmetic crossover for solving the economic dispatch and environmentally constrained economic dispatch problems. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating polici ..."
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Cited by 2 (0 self)
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This paper presents a new genetic approach based on arithmetic crossover for solving the economic dispatch and environmentally constrained economic dispatch problems. Elitism, arithmetic crossover and mutation are used in the genetic algorithm to generate successive sets of possible operating policies. The proposed technique improves the quality of the solution. The employed arithmetic crossover operation takes the real values presented by two individuals and produced new generation on the basis of the arithmetic mean. For economic dispatch problem, the new genetic approach is compared with an improved Hopfield NN approach (IHN) [1], a fuzzy logic controlled genetic algorithm (FLCGA) [2], an advance engineeredconditioning genetic approach (AECGA) [3] and an advance Hopfield NN approach (AHNN) [4]. The results of the proposed approach for environmentally economic dispatch are compared with the results of the Taboo Search (TS) [5], the Hopfield NN [5] and a neural networks approach [6].